1. Field
The present invention relates to a hierarchical latent variable model estimation device, a hierarchical latent variable model estimation method, a supply amount prediction device, a supply amount prediction method, and a recording medium.
2. Description of Related Art
A supply amount of a product in a store is accumulated data observed according to various factors. Here, sales is an example of a supply amount. Thus, the data is accumulated as observation values resulting not from one factor but from various factors. s another example, analyzing correlations between sales and weather and/or time of day enables reduction of running out of stock or overstocking. Examples of the supply amount include sales of products, shipments, sales proceeds, and the total sales amount of a store.
Accordingly, a technology for predicting future demands from past sales data (for example, see Japanese Patent No. 4139410 and Japanese Unexamined Patent Application, First Publication No. 2010-128779). Japanese Patent No. 4139410 discloses a technology for computing a proper amount of stock, using a prediction model according to information such as the day, date, or promotion information. Japanese Unexamined Patent Application, First Publication No. 2010-128779 discloses a technology for computing a sales using the optimal multiple regression type extracted based on information such as the number of marketing specialists, store area, traffic, or area population.
Further, a method for determining the type of observation probability by approximating, for a mixture model which is a typical example of a latent variable model, a complete marginal likelihood function and maximizing its lower bound (lower limit) is described in Ryohei Fujimaki, Satoshi Morinaga: Factorized Asymptotic Bayesian Inference for Mixture Modeling. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS), March 2012 and PCT International Publication No. WO 2012/128207.